Hello Image Classification

This tutorial is also available as a Jupyter notebook that can be cloned directly from GitHub. See the installation guide for instructions to run this tutorial locally on Windows, Linux or macOS. To run without installing anything, click the launch binder button.

This basic introduction to OpenVINO™ shows how to do inference with an image classification model.

A pre-trained MobileNetV3 model from Open Model Zoo is used in this tutorial. For more information about how OpenVINO IR models are created, refer to the TensorFlow to OpenVINO tutorial.

Imports

import cv2
import matplotlib.pyplot as plt
import numpy as np
from openvino.runtime import Core

Load the Model

ie = Core()
model = ie.read_model(model="model/v3-small_224_1.0_float.xml")
compiled_model = ie.compile_model(model=model, device_name="CPU")

output_layer = compiled_model.output(0)

Load an Image

# The MobileNet model expects images in RGB format.
image = cv2.cvtColor(cv2.imread(filename="../data/image/coco.jpg"), code=cv2.COLOR_BGR2RGB)

# Resize to MobileNet image shape.
input_image = cv2.resize(src=image, dsize=(224, 224))

# Reshape to model input shape.
input_image = np.expand_dims(input_image, 0)
plt.imshow(image);

Do Inference

result_infer = compiled_model([input_image])[output_layer]
result_index = np.argmax(result_infer)

# Convert the inference result to a class name.
imagenet_classes = open("../data/datasets/imagenet/imagenet_2012.txt").read().splitlines()

# The model description states that for this model, class 0 is a background.
# Therefore, a background must be added at the beginning of imagenet_classes.
imagenet_classes = ['background'] + imagenet_classes

imagenet_classes[result_index]

'n02099267 flat-coated retriever'